Parallel computing technique for EM modeling

1. 天津大学电子信息工程学院 School of Electronic Information Engineering Parallel computing technique for EM modeling makai

2. Contents 1、background 2、Basic theory of parallel computing 3、An example 4、plan School of Electronic Information Engineering Company name

3. Background • Arificial neural network (ANN) techniques have been recognized as a powerful tool in electromagnetic(EM)-based modeling and design optimization of microwave passive components . ANN can learn EM responses versus geometrical variables through an automated training process, and the trained ANN can be used as accurate and fast models in the design optimization. School of Electronic Information Engineering Company name

4. Background “Efficient design optimization of microwave circuits using parallel computational methods,” European Microwave Conference, Amsterdam, the Netherlands, Nov. 2012. “Parallel automatic model generation technique for microwave modeling,” in IEEE MTT-S Int. Microw. Symp. Dig., Honolulu, Hawaii, June 2007. School of Electronic Information Engineering Company name

5. Basic theory of parallel computing School of Electronic Information Engineering Company name

6. OpenMP Programming Model • Shared Memory Model Uniform Memory Access Non-Uniform Memory Access • OpenMP is designed for multi processor/core, shared memory machines School of Electronic Information Engineering Company name

7. MPI(message passing interface) Programming Model • Distributed Memory Model distributed memory architecture hybrid distributed shared memory architecture • MPI was designed for distributed memory architectures. • Basic MPI Concepts:A set of processes executing in parallel. These processes have separate address space. School of Electronic Information Engineering Company name

8. Parallel computing environment Figure 1 A Windows HPC Server cluster of workstations School of Electronic Information Engineering Company name

9. Performance Measurement for Parallel Computing • Speedup factor • Efficiency when E is given as a percentage School of Electronic Information Engineering Company name

10. Parallel Algorithm Design process Problem Partitioning Communication Agglomeration Mapping Figure 2 PCAM process School of Electronic Information Engineering Company name

11. An example of parallel computing School of Electronic Information Engineering Company name

12. An example of parallel computing • Problem description Sum = School of Electronic Information Engineering Company name

13. Figure 3 Data flow chat

14. Comparison of the running result Table1 Parallel on one computer School of Electronic Information Engineering Company name

15. Comparison of the running result Table2 Parallel on two computers School of Electronic Information Engineering Company name

16. Comparison of the running result Table3 Parallel on three computers School of Electronic Information Engineering Company name

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